Think Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation

Paper recorded by Signals 4 on 2026-10-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.AI · 人工智能 · first seen 2026-10-22

Abstract

Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the final prediction. We challenge this de facto design with two diagnostic findings. (1) Non-uniform computational need: items within the same sequence vary drastically in how much computation they require. Most converge after shall

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